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À qui appartient le 4 juillet ? L’indépendance américaine et sa mémoire
No full textPrésentation de l'éditeur : "Le 250e anniversaire de l’Indépendance des États-Unis est l’occasion d’une bataille culturelle acharnée opposant deux visions de la nation. La première, très conservatrice, promue par l’administration Trump, chante la gloire ancienne et exclusive des héros révolutionnaires. La seconde, nourrie des savoirs de l’histoire sociale et continentale, reconnaît à tous le droit à la liberté et à l’égalité inscrit dans la Déclaration d’indépendance adoptée le 4 juillet 1776. Cet ouvrage analyse ce texte fondateur de la République américaine et sa mémoire contestée pour mettre en perspective la crise politique qui divise aujourd’hui le peuple américain
SpeckSeq enables high-throughput functional stratification of MEFV variants in autoinflammatory diseases
No full textInternational audienceVariants of uncertain significance (VUS) are a major obstacle in genetic diagnosis, particularly when involving gain-of-function (GoF) mutations that are poorly predicted in silico. MEFV, which encodes the inflammasome sensor pyrin, is mutated in two autoinflammatory diseases, familial Mediterranean fever (FMF) and pyrin-associated autoinflammation with neutrophilic dermatosis (PAAND). Here, we developed SpeckSeq, a method that combines DNA bar-coding, ASC speck–based single-cell sorting and next-generation sequencing to systematically identify hypermorphic MEFV variants in response to different stimuli. SpeckSeq identified 49 GoF mutations separated into two distinct groups containing either PAAND variants or FMF variants. SpeckSeq was validated using patients’ cells and supported a reclassification of MEFV variant pathogenicity, leading to novel diagnoses. As a large-scale mutagenesis approach, using human genetics as a guide, SpeckSeq revealed structural and functional pyrin features, including a putative ligand-accommodating cavity in the B30.2 domain. Altogether, SpeckSeq classifies VUS to refine molecular diagnostics and improve our knowledge on the pyrin inflammasome
Evidence for the absence of a relationship between inflammation and cognition in a cohort of 1565 individuals with bipolar spectrum disorders: a Bayesian analysis of network
No full textInternational audiencePrevious studies have reported variable associations between peripheral inflammatory markers and cognitive functioning in individuals with bipolar spectrum disorders (BSD), with some identifying significant links and others finding no relationship. Such inconsistencies raise important questions about the role of inflammation in cognitive impairment among individuals with BSD. This study aims to investigate the relationship between peripheral inflammatory markers and cognitive function in a clinical sample of individuals with BSD using a Bayesian network analysis framework. We analyzed data from a large cohort (n = 1565) focusing on hsCRP and a subsample (n = 249) that included concurrent assessments of additional cytokines including Interleukin-6 and Tumor Necrosis Factor-alpha. A Bayesian approach was utilized to quantify uncertainty regarding the presence or absence of associations between inflammation and cognitive function. Our findings revealed no significant associations between inflammatory markers and cognitive performance in both samples. Strong evidence was found supporting the absence of association, with network analysis indicating distinct clusters for cognitive and inflammatory variables, suggesting they function as independent constructs with limited interactions. In our clinical sample of individuals with BSD, our findings do not support a direct association between some inflammatory markers and cognition, aligning with studies that found minimal or no associations. Our study emphasizes the importance of utilizing Bayesian methods to assess these relationships rigorously and suggests further exploration of individual differences and subgroup effects in future research
A Hybrid Framework for Intelligent Crisis Management in Parkinson's Disease
No full textInternational audienceParkinson’s disease (PD) is a chronic neurodegenerative disorder with progressive motor symptoms(tremor, rigidity, gait freezing, falls) and disabling non-motor features such as depression,anxiety, sleep problems. Patients may suddenly deteriorate (severe OFF episodes, unexpectedfalls...) and require rapid medical intervention, often outside hospital settings [1]. This createsa strong need for timely detection of crises and efficient dispatch of appropriate emergencyresources. Existing research on EMS in PD makes little use of longitudinal clinical informationand digital biomarkers, rely on triage decisions based on incomplete data, and do not fullyexploit modern connectivity (Internet of medical things, wearables, V2X) [2]. In this work,we propose an Intelligent Dispatch framework that connects smartphone-based monitoring,machine learning (ML), eXplainable AI (XAI) and optimization within the context of a crisisrelated to Parkinson’s disease. The framework aims to detect clinically relevant deteriorationin real time, classify patients into urgency levels, provide interpretable explanations ofrisk, and use these predictions to guide ambulance and hospital selection, ultimately reducingtime-to-intervention and improving coordination of emergency care for people with PD
Négocier l’espace de la production des savoirs sur la question raciale au Royaume-Uni : le cas de l’Institute of Race Relations (1952-1972)
No full textInternational audienc
Model selection for extremal dependence structures using deep learning: Application to environmental data
No full textAlthough the CLIC-based model selection approach is widely used to identify spatial extreme models, the complexity of the associated statistical inference limits the reliability of this criterion. In addition, the strong spatial dependence in small or moderate regions may lead to substantial overlap among the spatial extremes models. This potential overlap increases the risk of model misidentification. In this paper, we exploit the ability of Convolutional Neural Networks (CNNs) to extract spatial patterns in order to develop a CNN-based model selection framework. The proposed approach evaluates how well the dependence structure observed in the data matches the dependence patterns implied by competing models. Two identification strategies are considered. In the first strategy, both the max-stable model and its associated covariance function are identified simultaneously by a single CNN in a one-step procedure. In the second strategy, model identification is performed hierarchically. First, a CNN identifies the class of max-stable model, and then additional CNNs are trained for each model to determine the corresponding covariance function. The performance of the two strategies is evaluated through an extensive simulation study designed to reproduce the spatial dependence structure of 2-m air temperature data over Iraq, where strong dependence and model overlap are observed. The results demonstrate that the proposed CNN-based approach provides an effective alternative for model selection in spatial extremes
Central flashes during stellar occultations. Effects of diffraction, interferences, and stellar diameter
No full textInternational audienceCentral flashes may occur during stellar occultations by objects in the Solar System. Catalog diffraction effects on the flash with point-like stars, monochromatic waves, and different cases of spherical transparent atmosphere; describe the corrections due to stellar diameters. To describe diffraction, we used the Huygens principle, the Sommerfeld lemma, and the stationary phase method, and we treated the effects of finite stellar diameter using Clausius' theorem. For point-like stars, the central flash shape is that of the classical Poisson spot, but with a greater height. For tenuous atmospheres that cannot focus the stellar rays at the shadow center, the flash is amplified by the factor compared to the Poisson spot, where R_0 and r_0 are the object and the shadow radii, respectively. For denser atmospheres that can focus the rays at the shadow center, the flash peaks at 2π^2(R_ ̊m CF /λ_ ̊m F )^2 ϕ_⊥(0), where R_̊m CF is the central flash layer radius, λ_̊m F is the Fresnel scale, and ϕ_⊥(0) is the flux that would be observed at the shadow center without focusing. For isothermal atmospheres with scale height H, the height is 2π^2 (R_ ̊m CF H)/λ_ ̊m F ^2. Fringes surrounding the central flash are separated by λ_ ̊m P = λ_ ̊m F ^2/R_ ̊m CF , which is related to the separation between the primary and secondary stellar images. For a projected stellar diameter D_* ≫ λ_ ̊m P , the flash is described by complete elliptic integrals, and has a full width at half maximum of 1.14 D_* and a peak value of 8H/D_*. For Earth-based occultations by Pluto and Triton observed in the visible with point-like stars, diffraction causes flashes with very large heights of ∼104-10^5∼200 during Pluto and Triton occultations, respectively. Diffraction dominates when using millimeter wavelengths or longer. We discuss the effects of departure from sphericity, atmospheric waves, and stellar limb darkening
Exploring mechanisms leading to composition errors in monazite (CePO4) analysed with atom probe tomography
No full textInternational audienceMonazite (CePO4) is widely used in U-Th-Pb geochronology due to its reliable age determinations, although isotopic disturbances often require nanoscale investigation to better understand the mechanisms at play. Atom Probe Tomography (APT) offers unique capabilities for nanoscale chemical analysis and 3D atomic reconstruction but presents challenges for insulating materials such as CePO4, particularly due to oxygen loss during field evaporation. This study investigates the effects of laser wavelength, energy, metallic coatings and detection device on mass spectrum optimization and compositional accuracy in synthetic CePO4 samples. Results show that shorter laser wavelengths (260 nm) enhance peak resolution, particularly when combined with advanced reflectron configurations, as demonstrated with the LEAP 6000 XR. Chromium coatings further improve thermal dissipation and reduce noise levels. However, compositional measurements reveal systematic underestimation of oxygen and overestimation of P and Ce, likely influenced by preferential low-field element evaporation. These findings highlight the need to carefully tune experimental parameters to mitigate quantification biases and enhance the reliability of APT analyses for geological materials
